Process window analysis

ABSTRACT

A method for process analysis includes acquiring first inspection data, using a first inspection modality, with respect to a substrate having multiple instances of a predefined pattern of features formed thereon using different, respective sets of process parameters. Characteristics of defects identified in the first inspection data are processed so as to select a first set of defect locations in which the first inspection data are indicative of an influence of the process parameters on the defects. Second inspection data are acquired, using a second inspection modality having a finer resolution than the first inspection modality, of the substrate at the locations in the first set. The defects appearing in the second inspection data are analyzed so as to select, from within the first set of the locations, a second set of the locations in which the second inspection data are indicative of an optimal range of the process parameters.

FIELD OF THE INVENTION

The present invention relates generally to methods and systems used inthe manufacture of semiconductor devices, and particularly tooptimization of process parameters in such manufacturing.

BACKGROUND

Lithography is a key step in the process of manufacturing semiconductordevices. In this step, radiation is projected through a mask so as toexpose a layer of photoresist that has been deposited on the surface ofa wafer. The pattern that is printed on the photoresist in this mannerserves as a masking layer in forming circuit features on the wafer.Defects that may occur in the lithographic process are thus likely tocause defects in the corresponding circuit elements.

As the dimensions of integrated circuits decrease and patterns becomemore complex, defects and marginality in the features formed bylithography become increasingly problematic. To avoid (or at leastminimize) defects in the integrated circuit device, the lithographicparameters, such as focal adjustment and exposure level, must be setprecisely to the optimal values. The set of parameters that gives thebest results, i.e., the smallest number of critical defects and thehighest yield, is referred to as the “process window,” and systematictechniques for choosing these parameters are referred to generally asprocess window qualification.

Various methods for process window qualification have been described inthe patent literature. For example, U.S. Pat. No. 6,902,855 describes amethod of determining the presence of an anomaly in qualifying apattern, patterning process, or patterning apparatus used in thefabrication of microlithographic patterns. Practicing this method ontest wafers is said to enable the identification of spatial areas wherea process will fail first and candidate regions for carrying out defectinspection and metrology.

Design information may be applied in analyzing the inspection datacollected for the purpose of process window qualification. For example,U.S. Pat. No. 7,570,796, whose disclosure is incorporated herein byreference, describes methods and systems for utilizing design data incombination with inspection data, in which defects detected on a waferare binned by comparing portions of design data proximate positions ofthe defects in design data space.

SUMMARY

Embodiments of the present invention that are described hereinbelowprovide methods, systems and software for analyzing inspection data,which can be useful particularly in process window analysis andqualification.

There is therefore provided, in accordance with an embodiment of thepresent invention, a method for process analysis, which includesacquiring first inspection data, using a first inspection modality, withrespect to a substrate having multiple instances of a predefined patternof features formed thereon by a fabrication process in which different,respective sets of process parameters are applied in forming differentones of the instances. The first inspection data are processed so as toidentify defects at respective locations in the instances of thepattern, and characteristics of the defects identified in the firstinspection data are analyzed so as to select a first set of thelocations in which the first inspection data are indicative of aninfluence of the process parameters on the defects. Second inspectiondata are acquired using a second inspection modality having a finerresolution than the first inspection modality, of the substrate at thelocations in the first set. The defects appearing in the secondinspection data are analyzed so as to select, from within the first setof the locations, a second set of the locations in which the secondinspection data are indicative of an optimal range of the processparameters.

Typically, the pattern is formed on the substrate in accordance with apredefined design, and the method includes setting the processparameters for fabrication of devices in accordance with the designbased on the selected second inspection data. The substrate typicallyincludes a semiconductor wafer, and the fabrication process includeslithographic patterning of the semiconductor wafer, and the instances ofthe predefined pattern include dies defined by the lithographicpatterning. The process parameters may include a focal adjustment and anexposure level applied in the lithographic patterning.

In a disclosed embodiment, the first inspection modality is an opticalimaging modality, and the second inspection modality is an electronimaging modality.

In some embodiments, analyzing the characteristics of the defectsincludes clustering the defects into a plurality of bins correspondingto the respective locations of the defects in the instances of pattern,sorting the bins so as to prioritize the bins that are indicative of aninfluence of the process parameters on the defects, and selecting thefirst set of the locations from among the prioritized bins. Typically,the pattern is formed on the substrate in accordance with a predefineddesign, and clustering the defects includes defining the binsresponsively to respective features of the design occurring in the bins.Additionally or alternatively, sorting the bins includes identifying acorrelation in one or more of the bins between changes in at least oneof the process parameters and the defects in the one or more of thebins, and prioritizing the one or more of the bins responsively to theidentified correlation.

In a disclosed embodiment, analyzing the characteristics of the defectsincludes computing for the defects corresponding values of a utilityfunction that are indicative of an information content of the firstinspection data at the respective locations of the defects, andselecting the first set of the locations in response to the values ofthe utility function.

There is also provided, in accordance with an embodiment of the presentinvention, a method for process analysis, which includes acquiringinspection data with respect to a substrate having features formedthereon, and processing the inspection data so as to identify a set ofdefects at respective locations on the substrate. For the defects in theset, corresponding values of a utility function are computed. Thesevalues are indicative of an information content of the inspection dataat the respective locations of the defects. A defect from the set isselected in response to the values of the utility function. In aniterative process, the selected defect is removed from the set, thecorresponding values of the utility function are recomputed, and anotherdefect is selected in response to the values of the recomputed utilityfunction until a specified number of the defects have been selected. Therespective locations of the selected defects are then outputted.

Typically, the features include multiple instances of a predefinedpattern of features formed thereon by a fabrication process in whichdifferent process parameters are applied in forming different ones ofthe instances, and outputting the respective locations includesproviding an indication of an optimal range of the process parameters.Computing the corresponding values of the utility function may includeevaluating the information content of the inspection data with respectto the process parameters.

Additionally or alternatively, processing the inspection data includesclassifying the defects into a plurality of classes, wherein computingthe corresponding values of the utility function includes calculating anuncertainty of classification of the defects.

In disclosed embodiments, the inspection data include at least oneimage.

There is additionally provided, in accordance with an embodiment of thepresent invention, a system for process analysis, which includes a firstinspection machine, which is configured to acquire first inspectiondata, using a first inspection modality, with respect to a substratehaving multiple instances of a predefined pattern of features formedthereon by a fabrication process in which different, respective sets ofprocess parameters are applied in forming different ones of theinstances. A second inspection machine is configured to acquire secondinspection data with respect to the substrate, using a second inspectionmodality having a finer resolution than the first inspection modality. Aprocessor is configured to process the first inspection data so as toidentify defects at respective locations in the instances of thepattern, to analyze characteristics of the defects identified in thefirst inspection data so as to select a first set of the locations inwhich the first inspection data are indicative of an influence of theprocess parameters on the defects, and to cause the second imagingmachine to acquire the second inspection data at the locations in thefirst set. The processor is configured to analyze the defects appearingin the second inspection data so as to select, from within the first setof the locations, a second set of the locations in which the secondinspection data are indicative of an optimal range of the processparameters.

There is further provided, in accordance with an embodiment of thepresent invention, a system for process analysis, including aninspection machine, which is configured to acquire inspection data withrespect to a substrate having features formed thereon. A processor isconfigured to process the inspection data so as to identify a set ofdefects at respective locations on the substrate, to compute for thedefects in the set corresponding values of a utility function that areindicative of an information content of the inspection data at therespective locations of the defects, to select a defect from the set inresponse to the values of the utility function, and to iterativelyremove the selected defect from the set, recompute the correspondingvalues of the utility function, and select another defect in response tothe values of the recomputed utility function until a specified numberof the defects have been selected for output.

There is moreover, provided, in accordance with an embodiment of thepresent invention, a process window analyzer, including a memory, whichis configured to receive first inspection data, acquired using a firstinspection modality, with respect to a substrate having multipleinstances of a predefined pattern of features formed thereon by afabrication process in which different, respective sets of processparameters are applied in forming different ones of the instances, andto receive second inspection data with respect to the substrate,acquired using a second inspection modality having a finer resolutionthan the first imaging modality. A processor is configured to processthe first inspection data so as to identify defects at respectivelocations in the instances of the pattern, to analyze characteristics ofthe defects identified in the first inspection data so as to select afirst set of the locations in which the first inspection data areindicative of an influence of the process parameters on the defects, andto cause the second inspection machine to acquire the second inspectiondata at the locations in the first set. The processor is configured toanalyze the defects appearing in the second inspection data so as toselect, from within the first set of the locations, a second set of thelocations in which the second inspection data are indicative of anoptimal range of the process parameters.

There is furthermore provided, in accordance with an embodiment of thepresent invention, a computer software product, including anon-transitory computer-readable medium in which program instructionsare stored, which instructions, when read by a computer, cause thecomputer to receive first inspection data, acquired using a firstinspection modality, with respect to a substrate having multipleinstances of a predefined pattern of features formed thereon by afabrication process in which different, respective sets of processparameters are applied in forming different ones of the instances, andto receive second inspection data with respect to the substrate,acquired using a second inspection modality having a finer resolutionthan the first inspection modality. The instructions cause the computerto process the first inspection data so as to identify defects atrespective locations in the instances of the pattern, to analyzecharacteristics of the defects identified in the first inspection dataso as to select a first set of the locations in which the firstinspection data are indicative of an influence of the process parameterson the defects, and to cause the second inspection machine to acquirethe second inspection data at the locations in the first set. Theinstructions cause the computer to analyze the defects appearing in thesecond inspection data so as to select, from within the first set of thelocations, a second set of the locations in which the second inspectiondata are indicative of an optimal range of the process parameters.

The present invention will be more fully understood from the followingdetailed description of the embodiments thereof, taken together with thedrawings in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic, pictorial illustration of a system for processwindow qualification, in accordance with an embodiment of the presentinvention;

FIG. 2 is a flow chart that schematically illustrates a method forprocess window qualification, in accordance with an embodiment of thepresent invention;

FIG. 3 is a flow chart that schematically illustrates a method foridentifying sample defect bins for review, in accordance with anembodiment of the present invention; and

FIG. 4 is a schematic representation of a wafer map, in accordance withan embodiment of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS Overview

Process window qualification (PWQ) techniques commonly use a testsample, comprising a substrate on which multiple instances of apredefined pattern of features are formed using the target fabricationprocess. Different, respective sets of process parameters applied informing the different pattern instances. Specifically, in PWQ forsemiconductor wafer processing, the substrate is the wafer, and theinstances of the predefined pattern are dies defined by the process oflithographic patterning in accordance with the design of a targetdevice. Different sets of lithographic parameters, such as differentvalues of focal adjustment and/or exposure level, are applied to thedifferent dies. The resulting wafer is known as a “focus/exposurematrix” (FEM).

In general, the optimal process window will differ among differentdevices, depending on the details of the design and processes used infabrication. It is therefore often necessary to review the entire FEMwafer in order to find the process window that will give an acceptablenumber and types of defects. A FEM wafer, however, contains a tremendousamount of detail and will typically contain many defects of variousdifferent types. These defects will most commonly occur at locations indies at which the process parameters are outside or near the borders ofthe desired process window; but certain defects may occur, as well, evenin dies that turn out to be inside the process window.

Ideally, all of the defects in the FEM wafer should be identified,classified, and taken into account in setting the process window. Inpractice, however, such exhaustive evaluation would require anunreasonable expenditure of time and resources. Therefore, one of thekey challenges of PWQ is to apply inspection and computing resourcesselectively, to identify and classify critical locations on the FEMwafer, i.e., locations that are likely to be most informative withrespect to the process parameters and their influence on creation (oravoidance) of defects. Images of these selected locations may then beanalyzed in detail, at high resolution, and the process parameters forfabrication of the target device may then be determined based on theseselected images.

Some embodiments of the present invention that are described hereinaddress this challenge by applying two different modalities of waferinspection, and analysis of the inspection data provided by thesemodalities, in two successive stages. The inspection modalities maycomprise imaging modalities, such as optical and/or electron imaging,and the inspection data may thus comprise image data. Alternatively oradditionally, however, the principles of the embodiments describedherein may be applied using information and attributes extracted fromother sorts of inspection modalities and data.

Initially, first inspection data, in the form of a first image, forexample, are captured using a first inspection modality, with respect toa substrate, such as a FEM wafer, having multiple instances of apredefined pattern of features formed thereon by a fabrication processin which different, respective sets of process parameters are applied informing different instances. A computer processes the first inspectiondata to identify defects at various locations and to analyze thecharacteristics of these defects in order to select a set of locationsthat appear to be indicative of the influence of the process parameterson the defects. A second inspection modality, having a finer resolutionthan the first inspection modality, is then applied to acquire secondinspection data (such as further images) with respect to the substrateat the locations in the selected set. The computer analyzes the defectsappearing in the second inspection data so as to select a subset of thelocations that are indicative of the optimal range of the processparameters.

For example, an optical imaging modality may first be applied to theentire substrate (i.e., the entire FEM) in order to identify criticallocations, and an electron imaging modality, such as scanning electronmicroscopy, may then be applied to the locations identified by opticalimaging. (Alternatively, an optical inspection tool with higherresolution than the first optical inspection tool could be used in thesecond stage instead of the scanning electron microscope.) A scanningelectron microscope (SEM) has finer resolution than optical imaging, butits operation is time-consuming and costly. Optical imaging is fastenough to apply to the entire wafer.

Thus, the optical image of the FEM wafer may be acquired and analyzed inorder to select the first set of critical locations. SEM images are thenacquired only at these locations, and the SEM images are processedfurther in order to re-sort and select the subset of the locations foruse in actually setting the process window. The results of the SEMimaging and analysis may be processed automatically in order to choosethe optimal process window. Additionally or alternatively, these resultsmay be presented to a human operator for use in setting the processparameters.

Typically, the disclosed embodiments make use of design information inanalyzing the images (or other inspection data) and selecting thelocations to consider in setting the process window. This sort oftechnique is referred to generally as design-based binning. For thispurpose, the computer clusters the defects into bins corresponding tothe respective locations of the defects in the instances of pattern,taking into account the features of the design of the target device thatoccur at these locations. The computer sorts the bins so as toprioritize those bins that are particularly indicative of the influenceof the process parameters on defects in the FEM wafer at thecorresponding locations. For example, the computer may identify acorrelation in a certain bin between changes in a process parameter overa set of the dies and the appearance of defects in the bin from die todie. In this case, the bin will be prioritized as an exemplar of theinfluence of the parameter in question. The computer selects thelocations that are to be passed for further imaging and analysis fromamong the prioritized bins.

As noted earlier, for purposes of further inspection and analysis, thecomputer attempts to select the locations on the FEM wafer that willyield the most useful information regarding the influence of the processparameters on the occurrence of defects in the pattern. For thispurpose, the computer may calculate, for each defect location underconsideration, a utility function that is indicative of the informationcontent of the inspection data at this location. In some embodiments,the utility function is calculated iteratively over a set of defectsunder consideration, such that in each iteration, the defect found tohave the highest utility is selected and removed from the set. Thecomputer then recomputes the corresponding values of the utilityfunction for the remaining defects, selects the next defect, andproceeds in this manner until a specified number of the defects havebeen selected for further processing and/or output.

The embodiments described hereinbelow relate specifically to processwindow analysis for use in semiconductor device manufacture, and to theuse of optical and SEM imaging tools in such analysis. The principles ofthe present invention, however, may similarly be applied using otherinspection modalities and in the production of complex structures ofother types, on wafers or on other sorts of substrates. Such alternativeapplications are also considered to be within the scope of the presentinvention.

SYSTEM DESCRIPTION

FIG. 1 is a schematic, pictorial illustration of a system 20 for processwindow qualification (PWQ), in accordance with an embodiment of thepresent invention. A lithography machine 24 forms a FEM on a substrate,such as a wafer 22. Typically, in this step, the lithography machineuses a mask created in accordance with computer-aided design (CAD) data,as is known in the art, to form multiple dies on wafer 22, each with acopy of the pattern. The pattern on each die is formed with its own setof process parameters, such as focus and exposure settings, which varyover the wafer. Detailed methods for creating a suitable FEM of thissort are known in the art and are beyond the scope of the presentinvention.

A first imaging machine 26 forms and processes an optical image ofpatterned wafer 22. An “optical” image, in the context of the presentdescription and in the claims, means an image formed using visible,infrared, or ultraviolet radiation. Machine 26 may comprise, forexample, a UVision® optical inspection system, produced by AppliedMaterials, Inc. (Santa Clara, Calif.). Alternatively, any other suitableinspection machine may be used at this stage, as long as it is capableof acquiring inspection data with respect to wafer 22 with sufficientspeed and resolution. The inspection data may comprise images or otherinformation, which may take the form, for example, of a set ofdescriptors and/or parameters with respect to the inspected locations.

A second imaging machine 28 is used to form high-resolution images oflocations selected based on the image acquired by imaging machine 26.Machine 28 may comprise, for example, a SEM such as a SEMVision™ defectanalysis system, likewise produced by Applied Materials, Inc.

A process window analyzer 30 processes the images captured by machines26 and 28 in order to select the image locations that are considered tobe of greatest relevance for PWQ. Analyzer 30 is typically built on ageneral-purpose computer, comprising a processor 32 with a userinterface 34 and a memory 36, as are known in the art, with suitabledata links to the other elements of system 20. Although analyzer 30 isshown and described here as a single unit, the processing functionsdescribed herein may alternatively be distributed among multiplecomputers and/or multiple processors, and the terms “computer” and“processor” as used in the present description and in the claims shouldbe understood as including multi-computer and multi-processorimplementations. Typically, processor 32 is programmed in software tocarry out the functions that are described herein. This software may bedownloaded to processor 32 in electronic form, over a network, forexample. Additionally or alternatively, the software may be stored onnon-transitory computer-readable media, such as optical, magnetic, orelectronic memory. Further alternatively or additionally, at least someof the functions of processor 32 may be carried out by programmable orhard-wired logic.

As will be described in greater detail hereinbelow, analyzer 30 storesCAD data in memory 36 with respect to the pattern that is formed bylithography machine 24 on the dies of wafer 22, and applies the CAD datain processing the images acquired by imaging machines 26 and 28. Basedon the CAD data and the image acquired by imaging machine 26, processor32 identifies and analyzes characteristics of defects in the image andthus selects a set of locations on wafer 22 that appear to beparticularly indicative of the influence of the process parameters oflithography machine 24 on the defects. Processor 32 then instructsimaging machine 28 to acquire high-resolution images of wafer 22 atthese locations.

Processor 32 identifies and analyzes the defects appearing in theselatter images in order to select a subset of the defect locations thatare most indicative of the bounds of the optimal process window.Analyzer 30 outputs these images, along with relevant analysis andprocess data, via user interface 34 for consideration by an operator ofsystem 20 in setting production parameters for lithography machine 24.Additionally or alternatively, processor 32 may, on the basis of theimages and associated data, autonomously recommend or even set theproduction parameters.

Methods for Process Window Analysis

FIG. 2 is a flow chart that schematically illustrates a method forprocess window qualification, in accordance with an embodiment of thepresent invention. This method is described, for the sake of convenienceand clarity, with reference to the elements of system 20, as describedabove. Alternatively, the method may be implemented in other systemconfigurations, and particularly using other types of imaging machinesthat are known in the art.

The method begins with preparation of a suitable test wafer, at a samplepreparation step 40. At this step, lithography machine 24 prints thetarget pattern on multiple dies of wafer 22 with different combinationsof focal adjustment and exposure settings on different dies, thuscreating a focus/exposure matrix (FEM), as is known in the art. Thiswafer is inserted into inspection machine 26, which acquires an image ofthe FEM wafer, at a defect detection step 42. Typically, the imageacquired at this step is an optical image of the entire FEM, or at leastlarge parts of it. Inspection machine 26 and/or analyzer 30 processesthis image in order to identify defects in the pattern. These defectsmay include, for example, locations where as the result of non-optimalfocus and exposure settings, structures on the wafer either extendbeyond their expected bounds or do not reach their expected bounds, thuscreating potential short circuits and open circuits.

In order to choose defects of potential value in analysis of the processwindow, processor 32 eliminates “nuisance defects” from furtherconsideration, at a nuisance filtering step 44. Nuisance defects in thiscontext are those that are considered not to be relevant to analysis ofthe lithographic process window. Particle defects may fall into thiscategory, for example.

Analyzer 30 applies design (CAD) data with respect to the pattern on thewafer in clustering the defects, at a clustering step 46. This sort ofclustering is referred to as design-based binning (DBB): Processor 32clusters defects into groups, referred to as bins, such that all of thedefects in any given bin are situated on a similar CAD pattern withinthe die, and associates each bin with the type of design featureoccurring in the corresponding location, as indicated by the CAD data.Bins containing the same sort of design feature may be grouped togetherat this stage.

Based on the detected defects and the bins in which they are located,processor 32 prioritizes the bins, at a bin sorting step 48. The purposeof this step is to rank the bins by their respective likelihood ofproviding useful input in choosing settings of the process parameters.For this purpose, processor 32 may compare the appearance of defects ina given bin over multiple dies of the FEM, as a function of themodulation of the process parameters over those dies. In particular, theprocessor may seek correlations between changes in the appearance ofdefects in a given bin over multiple dies and the variations in valuesof the process parameters over the same dies. For example, if theoccurrence or severity of defects in a given bin over a set of diesincreases monotonically with the change in a given process parameterover this set of dies, then it is likely that the defects in questionare the result of changes in the parameter settings. On the other hand,defects whose appearance over such groups of bins does not change, orchanges in a manner that is not correlated with changes in the processparameters, are likely to be of less relevance. Thus, at step 48,processor 32 may sort the bins according to correlations of this sortbetween defects and process parameters. Moreover, in this step processor32 may generate a preliminary process window based on the correlationanalysis. This preliminary process window is a rough estimation, andwill be refined in subsequent steps, as described below.

Working from the sorted list of bins and the preliminary process window,processor 32 selects a set of defect locations for further review, at asampling step 50. For example, processor 32 may choose only a certainnumber of the locations that received high ranks at step for processingat step 50. Additionally or alternatively, processor 32 may factor theranking from step 48 into the selection may at step 50.

To select the locations at step 50, processor 32 computes values of autility function that are indicative of the information content of thedefects at the respective locations, based on the correlation betweenthe defects and the modulation of process parameters (according to dieand bin within the die), and selects the locations having the highestutility. The information of relevance in this case is the extent towhich each evaluated location is indicative of the edges of the processwindow and the confidence for each die to be within or outside theprocess window. In other words, the utility function is defined suchthat locations that are marginal in terms of process settings—i.e., atwhich the appearance of the defects indicates that the correspondingbins are neither squarely inside nor far outside the process window—willgenerally have the highest utility values. The utility values may becomputed and updated iteratively, so that each selected location is theone that gives the highest utility given the locations and defects thatwere selected before it. Details of this computation and selectionprocess are presented in FIG. 3.

Wafer 22 is now passed to imaging machine 28 for high-resolutioninspection, at a sample imaging step 52. As noted earlier, the imagesacquired at this step may be electron images, such as SEM images,although other high-resolution imaging modalities may alternatively beused, such as a high-resolution optical inspection machine. Typically,at step 52, imaging machine 28 scans only the sample locations that wereselected at step 50. Analyzer 30 processes the images acquired byimaging machine 28 in order to identify and classify the defects thatappear in them. Because of the superior resolution of imaging machine28, the defect classifications achieved at step 52 are generally moreaccurate and reliable than those found in the optical image at step 42,and provide greater confidence regarding the relationship between givendefects, the modulation of process parameters, and the CAD structure towhich they belong. Methods that may be used at this step are described,for example, in U.S. Patent Application Publications 2013/0279794,2013/0279790 and 2013/0279791, whose disclosures are incorporated hereinby reference.

Based on the defect classification results, processor 32 prioritizes thebins that were inspected at step 52, at a bin re-sorting step 54 andproduces a finer process window. Working from this new sorted list ofbins, processor 32 selects the final set of defect locations to be usedfor final review and setting of the process parameters, at a re-samplingstep 56. In general, steps 54 and 56 may use criteria and computationalmethods similar to those applied at steps and 50, as described above,with changes as appropriate in view of the different imaging parametersand classification information that is available.

The sample bins selected at step 56 are typically presented in a reportto an operator of system 20, with a suitable image of each sample binand values of the process parameters that were used in producing thecorresponding die in the FEM. The report may also include recommendedprocess settings or a range of such settings, based on automatedanalysis of the sample bins by processor 32. Based on this report, theprocess parameters to be used in producing the target design are chosen,at a process window setting step 58. The choice may be madeautomatically by analyzer 30 or manually by the system operator, or by acombination of automated and manual functions.

FIG. 3 is a flow chart that schematically shows details of sampling step50, in accordance with an embodiment of the present invention. As notedearlier, the method presented in FIG. 3 may be used, mutatis mutandis,in implementing re-sampling step 56. The key element in the method ofFIG. 3 is calculation of a suitable utility function for each location(defined in terms of die and bin in the FEM) within the set of thelocations that is under consideration, at a utility computation step 60.The utility function is indicative of the information content of thedefect location, specifically as the information pertains to the valuesof the process parameters under evaluation. As mentioned earlier, thisinformation can be derived from images or other attributes andparameters gathered from other sorts of inspection tools and machines.

In this regard, the inventors have found that a utility function basedon the uncertainty of classification of the defects gives good resultsin choosing a group of sample images that are informative with respectto the bounds of the process window. Such a utility function U couldhave the form:

$U = {- {\sum\limits_{{\omega_{D} = 0},1}{{P\left( {{f(D)} = \omega_{D}} \right)}\ln \mspace{14mu} {P\left( {{f(D)} = \omega_{D}} \right)}}}}$

In this equation, P is a probability function taken over the classifierfunction f for the given die D, giving the probability that the die willbe classified as being inside the process window (ω_(D)=0) or outsidethe process window (ω_(D)=1), given the classification of the currentdefect x_(s). The probability P may take into account informationgathered from various sources, such as defect images, a stepper machine,an inspection machine, a-priori knowledge of an expert, and previousanalysis of the sorting step.

The utility function U can take various forms. For example, the utilitycan be based on the decision of a classifier, which indicates how likelyany given defect is to be a defect of interest or a nuisance defect.Other methods that are known in the art, such as the generalized methodof moments (GMM), hidden Markov models (HMM), support vector machines(SVM), entropy estimation, or ensembles of classifiers, may also be usedin estimating the probability values and hence the utility functions.One set of functions that may be used for this purpose is presentedbelow in Appendix A (although this is just one example of a functionthat exhibits suitable functional behavior). Alternative utilityfunctions will be apparent to those skilled in the art after reading thepresent description and are considered to be within the scope of thepresent invention.

Returning to FIG. 3, after calculating the utility function for eachdefect location at step 60, processor 32 chooses the die/bin combinationthat has the highest value of utility, at a bin selection step 62. Inother words, the processor finds argmax(U) and chooses the correspondingdie D_(i) and bin B_(j). The processor selects a defect x_(s) from thisbin for inclusion in the set of samples that is to be passed on forfurther review, at a sample extraction step 64. Selection of a defectfrom a die with the highest utility is typically made according to astatistical analysis of the defect in an attribute space. Defects thathave better chance of being defects of interest are chosen within theselected die. The sampling algorithm that is used at this step may beimplemented as a look-ahead selective sampling method, as is known inthe art, which optimizes the selection of defects with maximum utilityby postulating the labels of certain dies as a result of futuredecisions.

The selected defect is now removed from the set of defects awaitingselection, and processor 32 checks whether the specified number ofdefects, M, to be output as sample defects have been selected, at acompletion checking step 66. If the specified number of defects has notyet been reached, processor 32 returns to step 60 and recomputes thevalues of the utility function, taking the possible classifications ofthe defects already selected as a given. The processor then proceedsthrough steps 62 and 64 to select another defect until M defects havebeen collected. Upon finding that the Mth defect has been selected atstep 66, processor 32 outputs the selected defects and their respectivelocations, at a sample output step 68. These samples are passed on forfurther review at step 52 or at step 58.

FIG. 4 is a schematic representation of a wafer map 70, illustrating thelocations of selected defects, in accordance with an embodiment of thepresent invention. Map 70 is divided into a matrix of dies 72,corresponding to the dies of the FEM wafer. Sample bins, containingdefects selected at step 50, are indicated by marks 74 in map 70. As canbe seen in the figure, these sample bins are concentrated in certaindies, which were found in steps 48 and 50 to be most informative withrespect to the process parameter settings.

It will be appreciated that the embodiments described above are cited byway of example, and that the present invention is not limited to whathas been particularly shown and described hereinabove. Rather, the scopeof the present invention includes both combinations and subcombinationsof the various features described hereinabove, as well as variations andmodifications thereof which would occur to persons skilled in the artupon reading the foregoing description and which are not disclosed inthe prior art.

APPENDIX Utility Function Computation

As noted earlier, a useful measure of the utility is:

$U = {- {\sum\limits_{{\omega_{D} = 0},1}{{P\left( {{f(D)} = \omega_{D}} \right)}\ln \mspace{14mu} {P\left( {{f(D)} = \omega_{D}} \right)}}}}$

Here P(ƒ(D)=ω_(D)) is the probability of a die D to be labeled ω_(D) bya classifier ƒ.

In the binary case P(ƒ(D)=1)=1−P(ƒ(D)=0), and P is calculated as avariant of a K nearest neighbor classifier (KNN) based on the distanceof the die D from the edge of the preliminary process window obtained inthe sorting step, with respect to being inside or outside of the processwindow.

Taking r to be the distance of die D from the preliminary process windowedge, P can then be calculated by the following equation:

${{P\left( {{f(D)} = 1} \right)}(r)} = {\frac{1}{2}{\left( {1 - {\tan \; {h\left( {\theta \; r} \right)}}} \right).}}$

The parameter θ is determined based on statistical analysis of each binregarding its contribution to the estimation of the preliminary processwindow. When a die is exactly on the edge

${{{P\left( {{f(D)} = 1} \right)}\left( {r = 0} \right)} = \frac{1}{2}};$

when a die is far from edge and outside the process windowP(ƒ(D)=1)(r=∞)=0; and when a die is far from the edge but inside theprocess window P(ƒ(D)=1)(r=∞)=1.

In this manner, the utility function reaches a maximum at the edges ofthe preliminary process window and thus increases the sample rate ofdefects in those dies having high uncertainty in their label, as towhether they are in or out of the process window.

1. A method for process analysis, comprising: acquiring first inspectiondata, using a first inspection modality, with respect to a substratehaving multiple instances of a predefined pattern of features formedthereon by a fabrication process in which different, respective sets ofprocess parameters are applied in forming different ones of theinstances; processing the first inspection data so as to identifydefects at respective locations in the instances of the pattern;analyzing characteristics of the defects identified in the firstinspection data so as to select a first set of the locations in whichthe first inspection data are indicative of an influence of the processparameters on the defects; acquiring second inspection data, using asecond inspection modality having a finer resolution than the firstinspection modality, of the substrate at the locations in the first set;and analyzing the defects appearing in the second inspection data so asto select, from within the first set of the locations, a second set ofthe locations in which the second inspection data are indicative of anoptimal range of the process parameters.
 2. The method according toclaim 1, wherein the pattern is formed on the substrate in accordancewith a predefined design, and wherein the method comprises setting theprocess parameters for fabrication of devices in accordance with thedesign based on the selected second inspection data.
 3. The methodaccording to claim 1, wherein the first inspection modality is anoptical imaging modality, and wherein the second inspection modality isan electron imaging modality.
 4. The method according to claim 1,wherein the substrate comprises a semiconductor wafer, and wherein thefabrication process comprises lithographic patterning of thesemiconductor wafer, and the instances of the predefined patterncomprise dies defined by the lithographic patterning.
 5. The methodaccording to claim 4, wherein the process parameters comprise a focaladjustment and an exposure level applied in the lithographic patterning.6. The method according to claim 1, wherein analyzing thecharacteristics of the defects comprises: clustering the defects into aplurality of bins corresponding to the respective locations of thedefects in the instances of pattern; sorting the bins so as toprioritize the bins that are indicative of an influence of the processparameters on the defects; and selecting the first set of the locationsfrom among the prioritized bins.
 7. The method according to claim 6,wherein the pattern is formed on the substrate in accordance with apredefined design, and wherein clustering the defects comprises definingthe bins responsively to respective features of the design occurring inthe bins.
 8. The method according to claim 6, wherein sorting the binscomprises identifying a correlation in one or more of the bins betweenchanges in at least one of the process parameters and the defects in theone or more of the bins, and prioritizing the one or more of the binsresponsively to the identified correlation.
 9. The method according toclaim 1, wherein analyzing the characteristics of the defects comprisescomputing for the defects corresponding values of a utility functionthat are indicative of an information content of the first inspectiondata at the respective locations of the defects, and selecting the firstset of the locations in response to the values of the utility function.10. A method for process analysis, comprising: acquiring inspection datawith respect to a substrate having features formed thereon; processingthe inspection data so as to identify a set of defects at respectivelocations on the substrate; computing for the defects in the setcorresponding values of a utility function that are indicative of aninformation content of the inspection data at the respective locationsof the defects; selecting a defect from the set in response to thevalues of the utility function; iteratively removing the selected defectfrom the set, recomputing the corresponding values of the utilityfunction, and selecting another defect in response to the values of therecomputed utility function until a specified number of the defects havebeen selected; and outputting the respective locations of the selecteddefects.
 11. The method according to claim 10, wherein the featurescomprise multiple instances of a predefined pattern of features formedthereon by a fabrication process in which different process parametersare applied in forming different ones of the instances, and whereinoutputting the respective locations comprises providing an indication ofan optimal range of the process parameters.
 12. The method according toclaim 11, wherein computing the corresponding values of the utilityfunction comprises evaluating the information content of the inspectiondata with respect to the process parameters.
 13. The method according toclaim 10, wherein processing the inspection data comprises classifyingthe defects into a plurality of classes, wherein computing thecorresponding values of the utility function comprises calculating anuncertainty of classification of the defects.
 14. The method accordingto claim 10, wherein the inspection data comprise at least one image.15. A system for process analysis, comprising: a first inspectionmachine, which is configured to acquire first inspection data, using afirst inspection modality, with respect to a substrate having multipleinstances of a predefined pattern of features formed thereon by afabrication process in which different, respective sets of processparameters are applied in forming different ones of the instances; asecond inspection machine, which is configured to acquire secondinspection data with respect to the substrate, using a second inspectionmodality having a finer resolution than the first inspection modality;and a processor, which is configured to process the first inspectiondata so as to identify defects at respective locations in the instancesof the pattern, to analyze characteristics of the defects identified inthe first inspection data so as to select a first set of the locationsin which the first inspection data are indicative of an influence of theprocess parameters on the defects, and to cause the second imagingmachine to acquire the second inspection data at the locations in thefirst set, wherein the processor is configured to analyze the defectsappearing in the second inspection data so as to select, from within thefirst set of the locations, a second set of the locations in which thesecond inspection data are indicative of an optimal range of the processparameters.
 16. The system according to claim 15, wherein the pattern isformed on the substrate in accordance with a predefined design, andwherein the processor is configured to compute the process parametersfor fabrication of devices in accordance with the design based on theselected second images.
 17. The system according to claim 15, whereinthe first inspection modality is an optical imaging modality, andwherein the second inspection modality is an electron imaging modality.18. The system according to claim 15, wherein the substrate comprises asemiconductor wafer, and wherein the fabrication process compriseslithographic patterning of the semiconductor wafer, and the instances ofthe predefined pattern comprise dies defined by the lithographicpatterning.
 19. The system according to claim 17, wherein the processparameters comprise a focal adjustment and an exposure level applied inthe lithographic patterning.
 20. The system according to claim 15,wherein the processor is configured to cluster the defects into aplurality of bins corresponding to the respective locations of thedefects in the instances of pattern, to sort the bins so as toprioritize the bins that are indicative of an influence of the processparameters on the defects, and to select the first set of the locationsfrom among the prioritized bins.
 21. The system according to claim 20,wherein the pattern is formed on the substrate in accordance with apredefined design, and wherein the bins are defined responsively torespective features of the design occurring in the bins.
 22. The systemaccording to claim 20, wherein the processor is configured to identify acorrelation in one or more of the bins between changes in at least oneof the process parameters and the defects in the one or more of thebins, and to prioritize the one or more of the bins responsively to theidentified correlation.
 23. The system according to claim 15, whereinthe processor is configured to compute for the defects correspondingvalues of a utility function that are indicative of an informationcontent of the first inspection data at the respective locations of thedefects, and to select the first set of the locations in response to thevalues of the utility function.
 24. A system for process analysis,comprising: an inspection machine, which is configured to acquireinspection data with respect to a substrate having features formedthereon; and a processor, which is configured to process the inspectiondata so as to identify a set of defects at respective locations on thesubstrate, to compute for the defects in the set corresponding values ofa utility function that are indicative of an information content of theinspection data at the respective locations of the defects, to select adefect from the set in response to the values of the utility function,and to iteratively remove the selected defect from the set, recomputethe corresponding values of the utility function, and select anotherdefect in response to the values of the recomputed utility functionuntil a specified number of the defects have been selected for output.25. The system according to claim 24, wherein the features comprisemultiple instances of a predefined pattern of features formed thereon bya fabrication process in which different process parameters are appliedin forming different ones of the instances, and wherein the processor isconfigured to output the respective locations of the defects with anindication of an optimal range of the process parameters.
 26. The systemaccording to claim 25, wherein the values of the utility function areindicative of the information content of the inspection data withrespect to the process parameters.
 27. The system according to claim 24,wherein the processor is configured to classify the defects into aplurality of classes, wherein the processor is configured to compute thecorresponding values of the utility function by calculating anuncertainty of classification of the defects.
 28. The system accordingto claim 24, wherein the inspection data comprise at least one image.29. A process window analyzer, comprising: a memory, which is configuredto receive first inspection data, acquired using a first inspectionmodality, with respect to a substrate having multiple instances of apredefined pattern of features formed thereon by a fabrication processin which different, respective sets of process parameters are applied informing different ones of the instances, and to receive secondinspection data with respect to the substrate, acquired using a secondinspection modality having a finer resolution than the first imagingmodality; and a processor, which is configured to process the firstinspection data so as to identify defects at respective locations in theinstances of the pattern, to analyze characteristics of the defectsidentified in the first inspection data so as to select a first set ofthe locations in which the first inspection data are indicative of aninfluence of the process parameters on the defects, and to cause thesecond inspection machine to acquire the second inspection data at thelocations in the first set, wherein the processor is configured toanalyze the defects appearing in the second inspection data so as toselect, from within the first set of the locations, a second set of thelocations in which the second inspection data are indicative of anoptimal range of the process parameters.
 30. A computer softwareproduct, comprising a non-transitory computer-readable medium in whichprogram instructions are stored, which instructions, when read by acomputer, cause the computer to receive first inspection data, acquiredusing a first inspection modality, with respect to a substrate havingmultiple instances of a predefined pattern of features formed thereon bya fabrication process in which different, respective sets of processparameters are applied in forming different ones of the instances, andto receive second inspection data with respect to the substrate,acquired using a second inspection modality having a finer resolutionthan the first inspection modality, wherein the instructions cause thecomputer to process the first inspection data so as to identify defectsat respective locations in the instances of the pattern, to analyzecharacteristics of the defects identified in the first inspection dataso as to select a first set of the locations in which the firstinspection data are indicative of an influence of the process parameterson the defects, and to cause the second inspection machine to acquirethe second inspection data at the locations in the first set, andwherein the instructions cause the computer to analyze the defectsappearing in the second inspection data so as to select, from within thefirst set of the locations, a second set of the locations in which thesecond inspection data are indicative of an optimal range of the processparameters.